Recent advances in earthquake catalog generation, through machine learning, template matching, and double difference techniques, now provide unprecedented resolution for investigating complex sequences, reveal their triggering mechanisms, and shed light on the underlying physical processes. This session will bring together studies of earthquake swarms and complex seismic sequences across diverse tectonic settings and scales.
We welcome contributions that focus on the analysis of earthquake swarms and complex seismic sequences in terms of spatio-temporal evolution, frequency-magnitude distribution, scaling properties, triggering mechanism, as well as laboratory and numerical modeling simulating the mechanical conditions yielding swarm-like and complex seismic sequences. Multidisciplinary studies integrating deformation data, geophysical imaging, geology, and fluid geochemistry are particularly encouraged. The overarching goal is to synthesize knowledge from various tectonic environments to improve our understanding of the physical processes governing complex seismic sequences.
For civil planning and hazard communication purposes, a central challenge during active seismic swarms is identifying the underlying causative source. This task is challenging because geodetic constraints on deformation at depth, especially in marine settings, are limited or poorly resolved. Therefore, it is essential to exploit the high spatial and temporal resolution provided by modern dense seismicity catalogues.
In early 2025, intense swarm seismicity between Santorini and Amorgos in the southern Aegean Sea triggered evacuations and heightened concern over volcanic and seismic hazards. The unrest occurred near the Santorini and Kolumbo volcanoes, and close to the rupture zone of the 1956 Mw 7.7 Amorgos earthquake, making it critical to determine whether the activity was driven by magmatic intrusion or tectonic fault slip.
We analysed ~25,000 earthquakes recorded over eight weeks using high-precision, machine learning–based relocation of seismic data. The resulting catalogue provides a detailed image of the space–time evolution of the swarm, including short-lived episodic tremor bursts. Relocated earthquakes are treated as virtual probes of stress change at depth to image candidate source processes under the assumption of Coulomb failure stress.
The seismicity defines a complex, migrating swarm at ~10 km depth. Dense swarm seismicity initiated northeast of Santorini and rapidly propagated ~20 km further northeast through mid-February, forming a widening, fan-shaped cloud. Coulomb stress imaging indicates horizontal magmatic dike propagation, rather than tectonic fault slip, as the dominant source of unrest. The intrusion is consistent with pump-like magma injections into newly opened dikes at ~12 km depth, producing multiscale, rebounding episodes of dike opening and triggered seismicity.
These results reveal a dynamic, feedback-driven mechanism for dike emplacement and demonstrate the potential of machine learning–enhanced stress imaging for tracking intrusions and improving eruption forecasting.
How to cite: Hicks, S., Lomax, A., Anagnostou, V., Papadimitriou, E., and Karakostas, V.: Unveiling the source of seismic swarms with Coulomb stress imaging: application to the 2025 Santorini-Amorgos, Greece seismic crisis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17452, https://doi.org/10.5194/egusphere-egu26-17452, 2026.
At the end of January 2025 a unique seismic sequence, in terms of intensity and earthquake magnitudes, started to evolve in the offsore area between Santorini and Amorgos. At the same time, GNSS timeseries recorded at Santorini showed the initiation of a rapid surface deformation phase with subsidence motion, precisely succeeding the six-months inflation period in the intra-caldera region of Santorini. Starting from February 1st and for the next 12 days seismicity increased sharply in rates and magnitudes reaching or exceeding 5.0, registering more than 210 events of M≥4.0 in a period of two weeks, more than twice fold the annual rate in the broader area of Greece. Herein, we use a high-resolution relocated catalogue to decipher the complex spatiotemporal evolution of the sequence and the physical process at play. The catalogue consists of more than 22,500 events, including 8,200 events detected by the routine analysis of the National Observatoty of Athens (NOA) complemented by several more obtained with a machine learning detection algorithm. Phase data for all the detected earthquakes were manually picked by the scientific staff of NOA and then processed with NonLinLoc and a local velocity model to obtain initial locations. All events were then relocated using the hypoDD algorithm, further constraining their location solutions. The bulk of relocated seismicity is constrained in an elongated SW-NE zone between Santorini and Amorgos, consistent with regional tectonics, mainly at depths of 5-15 km. The sequence evolved as a swarm displaying some unique migration patterns classified in distinct main phases of onward and backward propagation episodes. Examining more closely the spatiotemporal evolution of seismicity indicates a step-like propagation pattern characterized by secondary seismicity fronts trailed by aseismic backfronts. The main propagation front of seismicity is consistent with a model simulating lateral dyke propagation away from an over-pressurized magma chamber, while migration velocities versus duration for the various secondary fronts are comparable to the ones reported in a global dataset of various dyke-induced swarms that we compiled. The b-value spatial variations further indicate distinct high-low zones that can be induced by high differential stresses combined with higher crustal heterogeneities and increased pore-pressures at shallower depths. Overall, the analysis and results integrated with further observations are consistent with a lateral dyke intrusion in the Santorini-Amorgos rift zone facilitated by the regional tectonic fabric and inducing pronounced seismicity, with its finer details elucidating the dyke emplacement process.
Acknowledgements
We would like to thank all the personnel of the Institute of Geodynamics, National Observatory of Athens and especially the seismic analysis team and the technical staff for their tireless efforts in monitoring and responding to the Santorini - Amorgos seismic crisis.
How to cite: Michas, G., Karastathis, V. K., Mouzakiotis, E., Gkika, F., Daskalaki, E., and Chousianitis, K.: Deciphering the complex spatiotemporal evolution of the 2025 Santorini - Amorgos seismic sequence, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13258, https://doi.org/10.5194/egusphere-egu26-13258, 2026.
Lateral dyke intrusions are magma-filled fractures that propagate horizontally through the Earth's crust, posing significant hazards to local populations. Since 2020, four major lateral dyking events have forced the evacuation of at least 10,000 people (Bato et al., 2021, Lewi et al., 2025). Although the underlying physical processes are well established, current models are complex, and it is unclear which factors control lateral propagation speed and the movement of magma within the dyke.
By comparing data from intrusions from around the world, we show that the spatio-temporal patterns of seismicity and ground deformation are ubiquitous, and can be split into two phases:
Lateral propagation: The seismic events migrate, delineating the location of the lateral dyke tip. The migration speed decays with time and the ground deforms along the entire length of the dyke.
Widening post-arrest: After the dyke reaches its final lateral extent, it continues to open. Seismicity propagates back into the previously quiet regions, and the ground deforms at the distal end only.
We use these observations to motivate a two-stage model of dyke intrusion: lateral propagation followed by widening after arrest. The three-dimensional hydro-mechanical process associated with dyking can be reduced through scale separation to a single Partial Differential Equation (PDE) resembling the classical heat equation (Zia and Lecampion, 2020, Nordgren, 1972). Scaling this shows that a dyke fed by a constant pressure source grows as t1/2 while those fed by a constant flux grow as t1/5 (Bunger et al., 2013). By solving the PDE we determine the time-dependent dyke opening distribution and the resulting stress field. We compare predictions of seismicity rates and changing surface deformation to observations from seismology and geodesy. We show that dykes are driven by a near-constant source pressure throughout lateral propagation and that patterns of seismicity and surface deformation are a result of the changing widths of the dyke both during propagation and after arrest.
Once arrested, changes in the dyke's opening become confined to a zone near the lateral tip, shifting the observed ground deformation towards the distal end. We find static stress changes on faults surrounding the dyke cannot satisfactorily explain the observed spatio-temporal pattern of seismicity. During rapid stressing, seismicity rates depend on both the magnitude and rate of stress change (Heimisson et al., 2022). The observed spatio-temporal seismicity pattern corresponds well with locations of positive of stress change rates, reflecting the combined influence of deviatoric stressing and early-time poroelastic effects.
References:
Bato, M.G. et al. 2021, Geophysical Research Letters, doi:10.1029/2021GL092803.
Lewi, E. et al. 2025, Bulletin of Volcanology, doi:10.1007/s00445-025-01852-x.
Zia, H. & Lecampion, B., 2020, Computer Physics Communications, doi:10.1016/j.cpc.2020.107368.
Nordgren, R.P., 1972, Society of Petroleum Engineers Journal, doi:10.2118/3009-PA.
Bunger, A.P. et al., 2013, Earth and Planetary Science Letters, doi:10.1016/j.epsl.2013.05.044.
Heimisson, E.R. et al., 2022, Geophysical Journal International, doi:10.1093/gji/ggab467.
How to cite: Davis, T., Biggs, J., and Way, L.: Two-stage model of propagation and arrest explains ubiquitous patterns of dyke seismicity , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17976, https://doi.org/10.5194/egusphere-egu26-17976, 2026.
How to cite: Uthaman, M. and Jana, N.: Evolution of the 2015 Mw 7.9 Gorkha earthquake in Nepal Himalaya: Insights from local and teleseismic earthquake analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-826, https://doi.org/10.5194/egusphere-egu26-826, 2026.
Large earthquakes and the subsequent postseismic period are the most dramatic part of the seismic cycle that usually lasts hundreds to thousands of years. However, the fault dynamics which account for the postseismic events are yet to be fully understood. It is well known that aftershock evolutions can reveal the geometry and rupture process of the seismogenic fault. Repeating aftershock, a type of repeating earthquake, is an effective tool for studying the deep fault behavior after strong earthquakes. Here we selected templates from the National Earthquake Data Center catalog between three years before and one year after the mainshock origin time and then used the fast matched filter to detect missing earthquakes. Next we use the seismicity of repeating aftershock sequences (RASs) as a proxy to reveal postseismic slips following the four large earthquakes in the southeastern Tibetan Plateau. We find 136 RASs after the Lushan, Jiuzhaigou, and Jinggu mainshocks, whereas only one RAS was detected after the Ludian mainshock that occurred on a conjugate fault. The seismicity shows the aftershock migrated couples of minutes after the mainshocks while the RAS occurred a few hours later. This observation suggests the brittle faulting preceeded to the deep creeps. The deep creeps mainly follow a velocity-strengthening friction mode and decay with an Omori-law p-value of ~1. The results may indicate that the combination of fault healing and geometry together controls the deep fault behaviors. We develop two models to explain the evolution of fault dynamics after large earthquakes. Our results provide new insights into spatiotemporal fault evolutions after large earthquakes.
How to cite: Liu, S., Tang, C.-C., and Shen, X.: Deep postseismic creep following large earthquakes revealed by repeating aftershocks in the southeastern Tibetan Plateau, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18927, https://doi.org/10.5194/egusphere-egu26-18927, 2026.
Due to the collision between the Indian and Eurasian plates and the southeastward compression of the Tibetan Plateau, western Yunnan is one of the regions in China with the most active seismic and volcanic activities as well as the most complex fault structures. We first built a deep-learning-based high-precision earthquake catalog for the Tengchong volcanic field over the past decade and found that 1) ∼59% of the seismicity occurred as swarms but on faults aligned with the regional tectonic stress field; 2) all swarms contained fluid-diffusion-like migration fronts; and 3) a year-long swarm, including two ML 5.2 earthquakes within two months, revealed complex fluid-fault interaction. Combined with the historical occurrences of M >6 earthquake swarms around the Tengchong volcanic field, our observations suggest potential increased likelihood of swarms with large-magnitude earthquakes where large tectonic faults and magmatic systems intersect.
With the aid of machine-learning-based detection, we then outlined a complex 3D fault zone accommodating a small earthquake swarm near Yunlong city, western Yunnan, China from February to May 2013. Our results showed that the swarm initiated from a compressive stepover zone and subsequently activated a complex fault zone including six planar fault segments. The migration front of the swarm can be well-modeled by fluid diffusion, indicating the swarm was primarily driven by pressurized fluid. Within the stepover zone, complex and dense fractures act as conduits connecting the reservoir and fault zone, facilitating fluid flow. Meanwhile, the stress in the stepover zone tends to increase in response to the compression of the two boundary faults, which not only makes the stepover zone more susceptible to be triggered by those transient stresses but also forms a fluid pumping mechanism that drives fluids from the stepover zone into the complex fault zone.
By integrating a deep-learning-based phase picker and an improved match-and-locate algorithm, we constructed a high -precision foreshock catalogue for the 2021 Yangbi earthquake. Meanwhile, a high-resolution earthquake source region velocity structure for the 2021 Yangbi sequence was also inverted in our study. Our results suggest that natural fluid diffusion is likely a driver of the Yangbi foreshock sequence based on three lines of evidence: 1) regions with low Vs and relatively high Vp/Vs are widespread within the fault system; 2) earliest foreshocks exhibit diffusion-like migration front, and 3) foreshock evolution coincides with typical fault valving behavior, and a few low-frequency signals whose distribution coincides with high Vp/Vs patches was clearly identified, strongly suggesting that fluid diffusion influenced nucleation.
Therefore, we propose that the interaction between the widely distributed fluids and faults in western Yunnan may play a crucial role in the seismic activity.
How to cite: Li, H., Liu, M., Ma, Z., Tan, Y. J., and Zhang, M.: Interaction between Fluids and Faults Based on Seismicity: Case Studies in Western Yunnan, China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2207, https://doi.org/10.5194/egusphere-egu26-2207, 2026.
In 2020, an earthquake sequence occurred in the Haenam area of the southwestern Korean Peninsula, where 74 cataloged earthquakes were recorded within two weeks (26 April–8 May 2020). The activity was concentrated at ~20 km depth, where seismicity is relatively uncommon on the Korean Peninsula, and it comprised an unusually persistent sequence with dozens to thousands of events occurring over the two-week period. In addition, the largest event (MW 3.2) occurred about one week after the first cataloged event rather than at the onset of the sequence. Previous studies reported that hundreds of additional microearthquakes occurred during the same period, and some interpreted the activity as a swarm, citing the lack of a clear mainshock–aftershock decay, distributed seismicity, and hypocentral migration consistent with fluid diffusion. Here, we focus on a spatiotemporal analysis of the Haenam sequence. To ensure catalog completeness, we applied an improved template matching technique, resulting in an enhanced dataset of 1,345 events. We performed precise relocation and magnitude calibration for a subset of well-recorded events. To place the catalog on a consistent magnitude scale, we estimated magnitudes for the remaining events from S-wave peak amplitude ratios using relative-magnitude scaling. Relocated hypocenters define an E–W striking plane (strike ~92°, dip ~62°) consistent with strike-slip faulting. Spatiotemporal clustering identifies five distinct clusters. Within each cluster, seismicity is largely confined to the estimated rupture radius of the largest event in each cluster, consistent with aftershock-like behavior. Successive clusters preferentially initiate near the edge of the preceding rupture area, suggesting cascade-like triggering. We further observed a brief deepening of seismicity to 21.3–21.5 km only immediately after the mainshock (MW 3.2), implying a transient downward extension of the effective lower cutoff of seismicity in the lower crust. Using magnitudes on a consistent MW scale, the enhanced catalog yields a b-value of 1.05 ± 0.03. We also estimated seismic moments from MW using the moment–magnitude relation and found that the largest earthquake accounts for ~39.4% of the total seismic moment released, indicating a mainshock-dominated sequence. Our results demonstrate how high-resolution spatiotemporal analyses and magnitude calibration can clarify the geometry, clustering, and nucleation of small intraplate sequences on the Korean Peninsula.
How to cite: Kim, B., Hong, Y., Kim, G., and Sheen, D.-H.: Spatiotemporal patterns and nucleation of the 2020 Haenam earthquake sequence in the southwestern Korean Peninsula, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10113, https://doi.org/10.5194/egusphere-egu26-10113, 2026.
Klyuchevskoy Volcanic Group (KVG) is one of the World’s largest and most active clusters of subduction-zone volcanoes and hosts a large and very active trans-crustal magmatic system. In this study, we applied machine-learning–based detection to the data of the KISS temporary seismic experiment operated in 2015-2016 in order to obtain a detailed catalog of earthquakes associated with the KVG volcano-magmatic activity. Our approach resulted in more than 11,000 detections, approximately ten times more than the previous catalog based on manual picking.
The detected seismic activity is clustered in time and space with many earthquakes occurring in spatially localized swarms. Three main earthquakes clusters are clearly associated with major active volcanoes: Klyuchevskoy, Tolbachik, and Ushkovsky. We automatically classified earthquakes into volcano-tectonic (VT) and long-period (LP) events based on differences in their frequency content. All three clusters mentioned above are dominated by LP events. The largest cluster beneath Klyuchevskoy corresponds to the well-known KVG deep long-period (DLP) seismic activity. It is located at approximately 30 km below the surface (i.e., at the crust-mantle boundary) and contains more than 4,000 events that are strongly clustered in time. Two largest DLP bursts precede the re-activation of Klychevskoy in January 2016 and its eruption in April 2016. Two smaller clusters beneath Tolbachik, and Ushkovsky contain earthquakes located in the crust above 20 km depth.
We also computed the frequency–magnitude distributions for each of these volcanic LP earthquake clusters and found that they differ from the Gutenberg–Richter power law typical for regular tectonic earthquakes. Volcanic LP earthquakes are deficient in larger-magnitude events and exhibit a much steeper decay in their magnitude distributions. These deviations likely reflect differences in source processes and mechanisms between volcanic and tectonic earthquakes.
We also compared our results with the previously established catalog of seismo-volcanic tremors and found that detections of earthquakes and tremors at first order are anti-correlated in time. Therefore, we suggest that a complete characterization of seismic response to re-activation of a trans-crustal magmatic system requires simultaneous analysis of “discrete” earthquakes and “continuous” tremors with the former providing a very detailed illumination during periods of “quiescence” and the latter containing the information during the periods of significant activity within the plumbing system.
Overall, our study demonstrates the potential of AI-based workflows to efficiently process seismic records from dense seismo-volcanic networks recording simultaneously occurring various types of seismo-volcanic events.
How to cite: Lu, W., Shapiro, N. M., and Münchmeyer, J.: Dense Array and Machine Learning Reveal Detailed Relationship between Seismicity and Volcano Magmatic Activity beneath Klyuchevskoy Volcanic Group, Kamchatka, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10327, https://doi.org/10.5194/egusphere-egu26-10327, 2026.
Machine learning has dramatically expanded earthquake catalogs, but efficiently extracting meaningful patterns from these datasets remains challenging. We present an automated workflow integrating clustering detection, sequence classification, and migration analysis with minimal manual intervention.
Our framework combines two clustering algorithms to identify spatiotemporal earthquake groupings, classifies sequences based on characteristic features, and detects migration patterns.
We apply this workflow to California catalogs (Southern California relocated catalog, Northern California catalog, and QTM template-matching catalog) and Japanese subduction zone catalog based on the S-net seafloor observatory network. Results demonstrate robust identification of diverse sequence types across different tectonic settings and spatial scales. Migration analysis reveals widespread fluid-driven characteristics in California earthquake swarms and potential fluid activity in the forearc region of the Japanese subduction zone.
This automated approach provides consistent, reproducible results while uncovering patterns potentially missed in manual analysis. The workflow enables rapid characterization of seismic sequences, which can improved seismic hazard assessment in tectonically active regions.
How to cite: Cui, X., Li, Z., de Barros, L., and Jean-Paul, A.: Automated Detection, Classification, and Migration Analysis of Earthquake Sequences: Applications to California and Japanese Subduction Zones, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19809, https://doi.org/10.5194/egusphere-egu26-19809, 2026.
The Ojos del Salado volcano is the highest active volcano in the world, located in the southern Puna Plateau in the high Andes, and at the southern end of the Central Andean Volcanic Zone. Ojos del Salado has erupted in the Holocene and appears to be geothermally active as it is the most likely source of the hot springs that feed into the nearby Laguna Verde. Despite the Ojos del Salado’s size and recent activity, it is not closely monitored and little is known about the current state of this quiet giant. To remedy this, we deployed a passive network of 29 geophones on the flanks of Ojos del Salado and down to the Laguna Verde to record seismic activity at and near the volcano for the month of February, 2024. This data provides insight into the level of seismic activity, and from this we can draw some conclusions about the volcanic activity present at Ojos. We used machine learning techniques to detect events, these were then manually checked and compiled into a SEISAN catalogue. Initially 345 local events and 129 regional events were detected. Taking only events that were recorded on at least eight stations and could be relocated within the network we found that there remained 152 local events within the network. These relocated events formed two main clusters, one on the western flank of the summit and one just north of the summit, between the Laguna Verde and the Ojos del Salado. The magnitude of completeness of this relocated catalogue was -0.3ML, and the local magnitudes ranged from -1ML to 2.8ML. The locations and fault plane solutions of the events at the summit suggest north south extension and generally follow mapped faults in the area, and agree with the main regional stress axes. The smaller cluster just north of the volcano are oriented differently, we suggest that this cluster, which mainly occurred as a swarm on February 8th following two days of heavy rainfall, may be due to stresses caused by an increase in geothermal fluids supplied in part by the heavy rains joining the geothermal system and increasing the local pore pressure. The passive data collection has also allowed us to analyze the continuous seismic signal which shows a periodicity of 12 hours and 24 hours in frequency bands from 1 to 10Hz. This could be an indication of the effect of solid earth tides, temperature, or even rainfall on the seismicity at the Ojos del Salado volcano.
How to cite: Murray-Bergquist, L., Thorwart, M., Garcia Pina, A., Ulloa Correa, C., van Ginkel, J., and Beniest, A.: Linking Environmental Controls to Seismicity at Ojos del Salado Volcano, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16491, https://doi.org/10.5194/egusphere-egu26-16491, 2026.
This study focuses on a deep crustal earthquake swarm that has been occurring since February 2025 in northern Yamaguchi, southwestern Japan. The swarm is located at depths of 25–35 km, approximately 20 km deeper than typical earthquake swarms in Japan. Because direct geophysical observations in the lower crust are limited, deep crustal earthquake swarms provide an important observation for investigating seismogenesis in the deep crustal environment.
We performed hypocenter relocation of 3,886 earthquakes with M ≥ 0.0 that occurred between February and June 2025, using initial hypocenters from the Japan Meteorological Agency. The 3,871 relocated hypocenters show two north–south–aligned planar clusters over a spatial scale of ~5 km. The two clusters are separated by a ~1 km-wide low seismicity zone. Within each cluster, hypocenters are heterogeneously distributed, with localized dense and sparse regions.
To grasp the spatiotemporal characteristics in the swarm, we first investigated seismicity migrations along the strike and dip directions of each planar cluster. In both clusters, seismicity exhibits an overall migration from deeper to shallower areas. Along strike, migration is not simple but shows zigzag-like fluctuations, whereas along dip, seismicity typically migrates upward and downward at a rate of ~1 km/day.
Because seismicity migration appears to be active at ~14 days intervals, we qualitatively compared the timing of seismicity migration with tidal normal stress variations on the fitted planes. As a result, we found that periods of large temporal variations in normal stress, particularly between low tides, corresponded to episodes of seismicity migrations.
We further analyzed the multiple seismicity migrations. To evaluate multiple migration episodes, we treated each earthquake as a spatiotemporal origin and analyzed subsequent events within a fixed time window of several days. Almost all of the migration sequences can be explained by an isotropic pore-fluid pressure diffusion model. Estimated diffusivities of diffusive migrations range from 1.0 to 5.0 m2/s, comparable to values reported for volcanic earthquake swarms.
The Moho depth beneath the swarm area is estimated to be ~40 km, indicating that the swarm occurs in the lower crust just above the Moho. The swarm is located just beneath the Abu monogenic volcano group, where petrological studies suggest partial melting of the lower crust. Considering that the most recent eruption occurred ~8,800 years ago, fluids separated from a magma reservoir may persist and migrate upward. The complex deep to shallow seismicity migration, relatively large diffusivities, and tidal modulation suggest that this deep crustal earthquake swarm is driven by highly pressurized, low-viscosity fluids moving through a structurally and hydraulically heterogeneous swarm area in the lower crust, particularly during periods of tidal normal stress variations.
How to cite: Amezawa, Y., Yabe, S., Sawaki, Y., Imanishi, K., Miyazawa, M., Nishikawa, T., Nishimura, T., Nagaoka, A., Miyamachi, R., and Ohmi, S.: Deep crustal earthquake swarm and complex seismicity migrations in northern Yamaguchi, southwestern Japan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15500, https://doi.org/10.5194/egusphere-egu26-15500, 2026.
In subduction zones, seismicity exhibits pronounced space–time heterogeneity that has not been fully explained. While many possible mechanisms, such as frictional heterogeneities, plate geometry, and stress distribution, can lead to contrasting slip behaviors, the precise origin of these slip heterogeneities and their manifestation in space–time distribution of seismicity remain open questions. This is partly due to observational resolution limitations that hinder the detection of fine-scale processes.
For this reason, the north-central zone of Chile is of particular interest: due to the heterogeneities present at the plate interface, associated with the Juan Fernandez Ridge and the Challenger Fracture, where both bathymetric anomalies affect seismic coupling and stress distribution. It is a structurally complex area with abundant seismicity, the occurrence of large interplate earthquakes in recent decades (e.g., the 2015 Mw 8.3 Illapel earthquake and the 1971 Mw 7.8 La Ligua earthquake), and the presence of multiple seismic sequences and persistent seismic activity.
In this study, we conduct a detailed spatiotemporal analysis of seismicity in the North-Central Chile subduction zone, with emphasis on the identification and characterization of seismic clusters using onland seismic time series from the National Seismological Center (CSN), and from the S5 onland temporary seismic networks , and offshore Distributed Acoustic Sensing (DAS) data , obtained from submarine telecommunications cables (Abyss network), we investigated the nature of these clusters and their possible classification as (foreshock–)mainshock–aftershock sequences, seismic swarms, or repetitive characteristic earthquakes.
By applying deep-learning techniques for seismic phase detection, together with unsupervised learning methods for seismic clustering, we explore the temporal evolution of seismicity and evaluate the existence of unique or recurrent characteristic events through time. Our results aim to elucidate why seismicity in this region is highly heterogeneous and to identify the physical processes that control this variability.
How to cite: Flores, M. C., Peralta, T., Potin, B., Baillet, M., Ambrois, D., Rivet, D., and Ruiz, S.: Seismic heterogeneity and earthquake clustering in the North-Central Chile subduction zone , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19158, https://doi.org/10.5194/egusphere-egu26-19158, 2026.
Intraplate earthquakes occurring in the Korean Peninsula provide an important opportunity to investigate how pre-existing faults respond to the current background stress field in a low-seismicity region. This study shows that earthquakes often form sparse and linear clusters, suggesting a potential link between observed seismicity and pre-existing tectonic boudaries in the southern Korean Peninsula. Based on the 2024 ML4.8 Buan earthquake sequence, this study extends the analysis to multiple linear earthquake clusters distributed across the southwestern Korean Peninsula. Using high-resolution earthquake catalogs via deep learning methods, waveform-based clustering, focal mechanism analyses, and rate-and-state friction (RSF) simulations, we examined the conditions under which these linear clusters become seismically active. The combined analyses highlight the roles of fault orientation, fault interaction, regional stress conditions, and frictional properties in controlling intraplate seismicity. Preliminary results indicate that faults favorably oriented with respect to the regional stress field are more likely to rupture, whereas unfavorably oriented faults may require additional factors (e.g., fault complexity, significantly reduced frictional conditions) to generate earthquakes. By extending RSF-based fault stability analyses to the southeastern Korean Peninsula, this study also emphasizes the importance of interactions within discrete fault networks in governing earthquake occurrence in low-strain-rate intraplate settings.
How to cite: Han, J., Kim, S., Heo, D., and Kang, T.-S.: Intraplate Fault Stability Analysis using Rate-and-State Friction Simulation: Case studies on Spatially Aligned Earthquake Clusters in the Southern Korean Peninsula, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6653, https://doi.org/10.5194/egusphere-egu26-6653, 2026.
The ~900 km long Queen Charlotte Fault (QCF), which separates the Pacific and North American plates, is the fastest-slipping oceanic–continental transform fault on Earth. Since the early 20th century, six major earthquakes with moment magnitudes greater than 7.0 have struck along the QCF, posing significant hazard threats to western America and Canada. The most recent event, the 2013 Mw 7.5 Craig earthquake, ruptured the central segment of the QCF and has been proposed as the first reported oceanic interplate earthquake exhibiting supershear rupture (Yue et al., 2013, JGR, 10.1002/2013JB010594), attracting widespread attention within the seismological community. Nevertheless, owing to the lack of long-term near-field monitoring, the seismic behavior of this region remains poorly understood.
In this study, we analyzed data from a dense ocean-bottom seismometer (OBS) network (network code YI) deployed along the central QCF from late August 2021 to early September 2022. The network consists of 25 OBS stations with an average spacing of ~15 km, providing an exceptional opportunity to characterize microseismicity along the central QCF. Using PickBlue, a machine-learning–based phase picker trained on OBS data, we constructed a high-resolution seismicity catalog comprising 502 well-located earthquakes with moment magnitudes ranging from 1.0 to 3.3. Our catalog delineates a steeply dipping (75°–80°) subvertical fault plane and reveals distributed seismicity within the Pacific plate, suggesting that transpressive convergence along the central QCF is largely accommodated by slip on the dipping fault plane and by offshore deformation of the Pacific plate.
Furthermore, along the central QCF, a highly segmented seismic behavior was revealed. Two primary earthquake clusters were detected in the southern section near the epicenter of the 2013 Mw 7.5 Craig earthquake, whereas the northern section remains nearly aseismic. The most active cluster was located at the margin of the main coseismic rupture area and coincides with a slightly curved fault segment, which may have decelerated northward rupture propagation during the 2013 Craig earthquake while accommodating most deformation. Further south, in the largest coseismic slip region, an additional cluster is observed within the area of maximum coseismic slip, suggesting progressive stress reloading on the previously ruptured fault plane. To better understand the stress evolution of the 2013 Craig earthquake, we also relocated a 21-day local aftershock catalog recorded ~4 months after the mainshock (Walton et al., 2019, EPSL, 10.1016/j.epsl.2018.11.021). Notably, the spatial distributions of aftershocks and interseismic events display a pronounced complementary pattern in the largest coseismic slip region, with interseismic events distributed at the center of the rupture zone and aftershocks beautifully surrounding it. Together, these observations illuminate the stress evolution of the 2013 Craig earthquake from the postseismic to the interseismic period and provide new insights into understanding the seismic cycle.
How to cite: Liu, Y., Lange, D., and Grevemeyer, I.: Post- and inter-seismic behavior in the central Queen Charlotte fault: implications for earthquake cycle, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7435, https://doi.org/10.5194/egusphere-egu26-7435, 2026.
The Christiana-Santorini-Kolumbo volcanic field is the most volcanically active segment of the Aegean Arc, encompassing both the large, caldera system of Santorini, and the smaller, highly active submarine volcano Kolumbo to the northeast. In early 2025, the region experienced a strong seismic swarm, with activity concentrated in the Anydros area, between the islands of Santorini and Amorgos. The most widely accepted cause for the swarm so far is a dyke intrusion beneath the Anydros Ridge (e.g. 1, 2).
As part of the HYDROMOX project, the RRS Discovery conducted a multidisciplinary research cruise in March 2025, acquiring passive seismic data alongside heat flow measurements, hydrothermal fluid and gas samples, and ROV imagery across Santorini, Kolumbo, and the Anydros Ridge. Here, we present results from a local network of 25 short-period and broadband ocean bottom seismometers (OBS), deployed for approximately three weeks within the Santorini-Amorgos area during the waning phase of the seismic swarm in March 2025. Despite the swarm having peaked prior to deployment, the OBS array recorded over 140,000 seismic events across 23 days, resulting in ~20,000 well-located events. This local microseismic dataset allows the recording of smaller magnitude events with better spatial coverage than the land networks alone, particularly at shallow depths. Integrating these results with regional structural information and seismic velocity models allows us to further investigate the after-effects of the hypothesised dike intrusion and the activation of shallow fault networks. Our findings provide new insights into the post-peak dynamics of this complex volcano-tectonic seismic swarm in a densely populated and seismically active region of the Aegean.
References:
[1] Isken, M. P. et al. (2025). Volcanic crisis reveals coupled magma system at Santorini and Kolumbo. Nature 2025 645:8082, 645(8082), 939–945.
[2] Lomax, A. et al. (2025). The 2025 Santorini unrest unveiled: Rebounding magmatic dike intrusion with triggered seismicity. Science, 390(6775).
How to cite: Gregory, E., Bayrakci, G., Tary, J.-B., and Yeo, I.: Resolving the final phase of the 2025 Santorini-Amorgos seismic swarm using a local ocean bottom seismometer array, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20161, https://doi.org/10.5194/egusphere-egu26-20161, 2026.
Deep low-frequency earthquakes (DLFs) have been observed at a depth of about 30 km near the Moho around active volcanoes. Source process of DLFs is thought to be related to the deep magma supply system (e.g., Nakamichi et al. 2003) and the number of them sometimes increase several months before an eruption. Therefore, DLFs is a key information for understanding the magma supply process from deep to shallow (e.g., Shapiro et al. 2017). The Iwate volcano, which located in Northeastern part of Japan, has experienced a volcanic unrest such as abnormal crustal deformation and increase of seismicity including DLFs since February 2024. In this study, we investigated the seismicity and focal mechanisms of DLFs in the Iwate volcano based on the matched filter method (e.g., Gibons and Ringdal, 2006) and waveform inversion.
We performed matched filter analysis for the period from January 2019 to December 2025. We used 175 template events relocated by Kurihara and Obara, (2021). This analysis detected approximately 8000 events over a 6-year period. The Iwate volcano has three DLF clusters: 10 km depth just beneath the volcano, 30 km depth of northern and southern parts of it. Among them, seismicity of DLFs in the north cluster has extremely increased since August 2024 and the number of them has also increased in the shallow cluster since three months after the activation of northern cluster. This result suggests the possibility that magma supply from deep to shallow areas has continued.
Then, we estimated source time functions and moment tensor components for template events based on the procedure of Aso and Ide (2014). Obtained moment tensors have various orientations and significant compensated linear vector dipole component, which is consistent with the previous study of DLFs in the Iwate volcano (Nakamichi et al. 2003). However, comparing focal mechanisms and seismicity, we found that DLF activity is mainly composed of events detected from specific templates. This result suggests the existence of a dominant local stress field such as the shape of stagnated magma near the Moho. As a preliminary interpretation, activation of DLFs can be caused by the volumetric deformation associated with intrusion into the magma.
How to cite: Oikawa, G.: Seismicity and focal mechanisms of deep low-frequency earthquakes in the Iwate Volcano, Northeast Japan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8624, https://doi.org/10.5194/egusphere-egu26-8624, 2026.
We have investigated the spatial and temporal variations in stress drop during a series of earthquakes, from the swarm to the early aftershocks in the Noto Peninsula, central Japan. The swarm began in 2018 and lasted more than five years until the devastating 2024 Mw 7.5 Noto earthquake. We estimated stress drop from a comparison between the observed and theoretical Frequency Index (FI). Employing a commonly used source model and the relations between corner frequency and stress drop, the theoretical FI is a function of S-wave velocity, attenuation factor, and stress drop. By assuming a S-wave velocity and using the separately measured attenuation factor, the theoretical FI depends solely on stress drop. We estimate the stress drop that minimizes the difference between the observed and theoretical FI. We validated the method by comparing the obtained stress drop with that from the ordinary method to measure the corner frequency. The result is consistent, though our method gives slightly higher stress drop than a one-to-one relationship. As long as we discuss the relative values of stress drop, this difference has little effect on subsequent observations and interpretations.
We estimated stress drop for 3,490 earthquakes with magnitudes from 1.5 to 4.0. The obtained stress drop shows an apparent spatial variation: Low-Stress-Drop Events (LSDEs) are dominant in the southern part of the swarm area, whereas both LSDEs and High-Stress-Drop Events (HSDEs) coexist in the northern part. Previous studies classified the swarm area into four subareas of S (southeast), W (southwest), N (northwest), and E (northeast). The swarm areas temporarily expanded, including subareas in this order. Our results show that earthquakes in the S subarea, where the swarm started, mostly have low stress drop. Some events with extremely low stress drop exhibit a unique waveform with a low-frequency band and a decaying amplitude over time, resembling volcanic low-frequency earthquakes. Previous studies have suggested that crustal fluids contribute to seismogenesis in the area. Our results give further and strong support for this suggestion. HSDEs mainly occurred in subarea N, with the highest seismicity, and in subarea E, which hosted many large swarm earthquakes. They are located sandwiched between and around the band of LSDEs. During the early aftershock stage, HSDEs are absent in these locations. The rupture of the mainshock originated in these subareas and was "quiet," with only minor moment release. Previous studies suggested that these subareas experienced strain release preceding the mainshock due to the swarm. The spatio-temporal variation of HSDEs is consistent with the interpretation. On the other hand, LSDEs in subarea S occurred during both the swarm and the aftershock sequences, implying continuous fluid supply from the anticipated fluid source beneath the swarm area. Our method of stress drop estimation does not aim for high accuracy, but rather to obtain estimates for many earthquakes with acceptable accuracy. The results of this study indicate that the approach works well to investigate the seismogenesis of complex earthquake sequences.
How to cite: Kosuga, M. and Maeda, T.: Spatio-temporal evolution in stress drop during earthquake sequences from the swarm to aftershocks in the Noto Peninsula, central Japan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8786, https://doi.org/10.5194/egusphere-egu26-8786, 2026.
The SNUCI seismic network was deployed in the Highlands of Central Iceland in July 2024, with the aim of investigating the crustal structure and local seismicity along the E-W aligned plate boundary segment between the Western and Eastern Volcanic rift Zones. The network consists of 15 broadband seismic sensors distributed over an area of ~75x150 km, surrounding the partially subglacial Langjökull and Hofsjökull volcanic systems. Complemented by stations operated by the Icelandic Meteorological Office and the University of Cambridge & University of Iceland, it is the densest local seismic array in the region to date and thus provides the opportunity to significantly improve our knowledge of crustal processes and monitor seismic activity within two large volcanic systems. Whereas the Langjökull system has exhibited sustained levels of seismicity detected by the national seismic monitoring network over the past decades, seismicity within the Hofsjökull system has increased markedly since 2020, prompting enhanced monitoring in the region.
Here we present the seismic catalogue from the first year of the SNUCI deployment. Automated event detection, relative relocation and clustering analysis are applied to provide a detailed description of the seismicity distribution and event characteristics in different earthquake clusters. Source mechanisms for the highest-magnitude events are constrained by P-wave first-motion polarity inversion. The catalogue shows persistent seismicity both within and outside of the known volcanic systems. While most activity is concentrated in the shallow crust (<15 km), several deep event clusters are identified in the lower crust beneath the Hofsjökull volcanic system, extending down to ~30 km depth. Our new high-resolution dataset can provide novel insights into the ongoing volcano-tectonic processes in this understudied region.
How to cite: Allas, H.-R., Jenkins, J., Winder, T., Ágústsdóttir, T., Guðnason, E. Á., Heimisson, E. R., Brandsdóttir, B., and Rawlinson, N.: Seismic Network in the Underexplored Central Iceland (SNUCI): Investigating Local Seismicity in the Langjökull and Hofsjökull volcanic systems , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14712, https://doi.org/10.5194/egusphere-egu26-14712, 2026.
Earthquake clustering methods based on nearest-neighbour distances provide a powerful framework for identifying seismic sequences and distinguishing clustered from background seismicity. Classical formulations rely on spatial proximity and therefore require accurate hypocentral locations, which limits their applicability to single-station template-matched catalogues with only a subset of located earthquakes. Here we introduce a new clustering approach that replaces spatial distance by waveform similarity, quantified through normalised cross-correlation, while retaining the established time–magnitude scaling of nearest-neighbour methods. Waveform similarity is treated as a distance in a feature space, and its effective dimension is estimated directly from the data using a maximum-likelihood intrinsic-dimension estimator. This allows the definition of a similarity-based nearest-neighbour distance with a clear statistical interpretation as the expected number of background events in time–similarity–magnitude space.
The method is specifically designed for single-station template-matched catalogues, where waveform similarity provides constraints on source proximity and fault association. We test the approach on natural seismic sequences in Switzerland using template-matched catalogues and benchmark the results against clusters obtained from double-difference relocated catalogues. This study aims to assess whether waveform similarity can robustly replace spatial distance in nearest-neighbour clustering while preserving the statistical and physical interpretability of the method, and to evaluate its potential for the analysis of dense template-matched catalogues from sparse or single-station seismic deployments.
How to cite: Kraft, T., Simon, V., and Toledo, T.: Replacing spatial distance with waveform similarity in nearest-neighbour earthquake clustering , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8019, https://doi.org/10.5194/egusphere-egu26-8019, 2026.
Tectonic earthquake swarms deviate significantly from the spatio-temporal evolution characteristic of mainshock-aftershock sequences. While earthquake sequences often begin with a dominant event called a mainshock, followed by a decaying rate of aftershocks governed by the Omori-Utsu law, earthquake swarms are defined by a gradual escalation of seismic activity lacking a singular, triggering large earthquake at the start of the cluster. In these sequences, peak magnitudes often emerge mid-sequence or later, frequently accompanied by distinct spatial migration and episodic bursts. This complex clustering evolution is driven by the interaction between steady tectonic loading and transient, short-term forcing mechanisms. Identifying these phenomena requires robust, unsupervised methodologies—a need that has spurred the development of various detection algorithms over recent decades.
This research provides a systematic evaluation of prevalent cluster-detection techniques and sequence characterization via the release of seismic moment over time. We apply four well-known (de-)clustering algorithms to identify clusters in space-time-magnitude space; subsequently, we evaluate each cluster using the statistical moment of the cluster source time function (i.e., the release of seismic moment over time). By utilizing thousands of synthetic catalogs generated through Epidemic-Type Aftershock Sequence (ETAS) modeling with time-varying background rates, we simulate realistic swarm behavior to test these tools. This synthetic framework allows us to define parametric boundaries that robustly differentiate swarms from mainshock-aftershock clusters. We then validate our findings against well-documented real-world datasets, including the 2010–2014 Pollino sequence and the Húsavík-Flatey transform fault in Northern Iceland. Additionally, show that the cluster classification to distinguish swarms from mainshock-aftershock sequences via the proposed statistics depends on the type of (de-)clustering algorithm used and, most importantly, on the cluster duration. Accordingly, our results highlight that real-world application remains sensitive to algorithm choice and catalog completeness, suggesting that human oversight is still essential for precise swarm characterization and interpretation.
How to cite: Passarelli, L., Petersen, G., Mizrahi, L., and Cesca, S.: Detecting and characterizing swarm-like seismicity, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6631, https://doi.org/10.5194/egusphere-egu26-6631, 2026.
The East Eifel Volcanic Field (EEVF) in Germany is a densely monitored yet dormant distributed volcanic field comprising hundreds of Quaternary volcanoes, including the most recent eruption of the Laacher See Volcano ~13,000 years ago. The GFZ's Central European Volcanic Province Observatory (CVO) integrates data delivered by multiple partners: Seismic data, GNSS, a superconducting gravimeter, and fluid monitoring sites to detect subtle signals of volcanic and tectonic activity. This study focuses on microseismic swarms observed within the EEVF since January 2020. We present a multidisciplinary analysis of selected swarm sequences, with particular emphasis on their spatial proximity to recently mapped crystal velocity anomalies, interpreted as potential melt reservoirs. Notably, the suspected locations of partial melt reservoirs beneath the EEVF correlate well with the location of the DLF earthquakes. Most recently, a swarm of approximately 120 locatable events (Mw < 1.6) occurred near the Laacher See Volcano in October 2025, prompting considerable public and media interest. Preliminary moment tensor inversion and cross-correlation-based clustering indicate a highly self-similar sequence with oblique normal faulting to strike-slip faulting mechanisms in agreement with the regional stress field. Although the EEVF is not typically characterized by extensive swarm activity, our analysis reveals tens of tiny swarm sequences over the past six years. The Oct-2025 swarm illustrated the feasibility of a quick, multidisciplinary assessment of the EEVF; during this swarm, no co-seismic changes in ground deformation, fluid properties, or gravity were detected. In our ongoing work, we explore several hypotheses linking the microseismic swarms to fluid-driven processes near potential melt reservoirs.
How to cite: Petersen, G., Laumann, P., Isken, M., Dahm, T., Deng, Z., Woith, H., Voigt, C., Zimmer, M., Hensch, M., Zeckra, M., Schmidt, B., and Zhang, H.: Microseismicity swarm activity in the East Eifel Volcanic Field - A response to magmatic processes?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2824, https://doi.org/10.5194/egusphere-egu26-2824, 2026.
